Channels are in relation according to a number of variables that are set adaptively such as the allowable average alignment difference between two discontinuity events. This explosive growth in accumulated data has generated an urgent need for new techniques and tools that can automatically support engineers and data analysts in transforming the enormous amounts of data into useful information and knowledge. This method is implemented and tested against real automotive emission test data. Jumps in data are detected using wavelet coefficients that are calculated dynamically for every level of wavelet coefficients. In this book, we introduce a novel method for analyzing multidimensional data sets and discovering index vectors by the use of discontinuity detection. The discovered index vectors of the analyzed channels (raw data) are related in a dynamic alignment of tree-like structure. The rapid increase in generating and collecting data with high resolution and recording frequency due to the availability and affordability of measurement technology is resulting in huge data archives that are growing exponentially in size.